{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b8821e7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "from torch.utils.data import DataLoader\n",
    "import matplotlib.pyplot as plt\n",
    "from torchvision.models import resnet18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "13ec155c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入和预处理数据\n",
    "batch_size = 16\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "transform = transforms.Compose([\n",
    "    transforms.Resize((224, 224)),  # 调整图像大小\n",
    "    transforms.Grayscale(num_output_channels=3),  # 将灰度图像转换为3通道\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5,), (0.5,))\n",
    "])\n",
    "\n",
    "trainloader = DataLoader(\n",
    "    torchvision.datasets.FashionMNIST('data', train=True, download=True, transform=transform),\n",
    "    batch_size=batch_size, shuffle=True)\n",
    "\n",
    "testloader = DataLoader(\n",
    "    torchvision.datasets.FashionMNIST('data', train=False, download=True, transform=transform),\n",
    "    batch_size=batch_size, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b81d0222",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lxj\\AppData\\Roaming\\Python\\Python39\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "C:\\Users\\lxj\\AppData\\Roaming\\Python\\Python39\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "# 定义ResNet模型\n",
    "model = resnet18(pretrained=False, num_classes=10).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "da022700",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义损失函数和优化器\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "786640c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5, Train Loss: 0.4395, Train Accuracy: 83.96%\n",
      "Epoch 2/5, Train Loss: 0.2767, Train Accuracy: 89.96%\n",
      "Epoch 3/5, Train Loss: 0.2307, Train Accuracy: 91.49%\n",
      "Epoch 4/5, Train Loss: 0.1931, Train Accuracy: 92.97%\n",
      "Epoch 5/5, Train Loss: 0.1635, Train Accuracy: 94.07%\n"
     ]
    }
   ],
   "source": [
    "# 训练模型\n",
    "num_epochs = 5\n",
    "train_losses = []\n",
    "train_accuracies = []\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()\n",
    "    running_loss = 0.0\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    for i, data in enumerate(trainloader, 0):\n",
    "        inputs, labels = data\n",
    "        inputs, labels = inputs.to(device), labels.to(device)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        outputs = model(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        running_loss += loss.item()\n",
    "        _, predicted = torch.max(outputs, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "        \n",
    "    train_loss = running_loss / len(trainloader)\n",
    "    train_accuracy = 100 * correct / total\n",
    "    \n",
    "    train_losses.append(train_loss)\n",
    "    train_accuracies.append(train_accuracy)\n",
    "    \n",
    "    print(f\"Epoch {epoch+1}/{num_epochs}, Train Loss: {train_loss:.4f}, Train Accuracy: {train_accuracy:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "770da366",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化训练损失和准确率\n",
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "# Plot train loss\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(train_losses, label='Train Loss', color='blue')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Train Loss over Epochs')\n",
    "plt.legend()\n",
    "\n",
    "# Plot train accuracy\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(train_accuracies, label='Train Accuracy', color='green')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy (%)')\n",
    "plt.title('Train Accuracy over Epochs')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "492c2e31",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
